Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence

Annotating protein sequences according to their biological functions is one of the key steps in understanding microbial diversity, metabolic potentials, and evolutionary histories. However, even in the best-studied prokaryotic genomes, not all proteins can be characterized by classical in vivo, in vitro, and/or in silico methods-a challenge rapidly growing alongside the advent of next-generation sequencing technologies and their enormous extension of 'omics' data in public databases. These so-called hypothetical proteins (HPs) represent a huge knowledge gap and hidden potential for biotechnological applications. Opportunities for leveraging the available 'Big Data' have recently proliferated with the use of artificial intelligence (AI). Here, we review the aims and methods of protein annotation and explain the different principles behind machine and deep learning algorithms including recent research examples, in order to assist both biologists wishing to apply AI tools in developing comprehensive genome annotations and computer scientists who want to contribute to this leading edge of biological research. Different principles and methods to functionally characterize and annotate prokaryotic proteins are reviewed, with a special emphasis on machine learning and deep learning algorithms.

Automated summary

Annotating protein sequences is crucial for understanding microbial diversity and potential applications, but traditional methods are limited by the vast amount of 'omics' data. Hypothetical proteins (HPs) are a knowledge gap with hidden potential. Artificial intelligence (AI) and machine learning algorithms offer opportunities to leverage 'Big Data' for comprehensive genome annotations. This review explores the aims, methods, and recent research examples of protein annotation, with a focus on machine and deep learning algorithms.